Various front-end components of wireless receiver systems, such as low-noise amplifiers or mixers, frequently introduce nonlinearities into the received signals, which manifest themselves in adjacent frequency bands. Because nonlinearities result in a lower Signal-to-Noise Ratio (SNR) of the received signal, receiver systems often include dedicated components designed to compensate for these nonlinearities prior to demodulating and decoding the received signal.
Some existing compensation schemes assume the transmission of a pilot sequence to estimate nonlinearity. However, in many applications, it is impossible or impracticable to send pilot signals which a receiver system may use to construct the corresponding nonlinearity model. Accordingly, it is extremely difficult to compensate for nonlinearities in such systems.
Additionally, some conventional approaches to compensating nonlinearities rely on a Volterra-series model of nonlinearity and require that the coefficients of the Volterra-series inverse be determined. As the order of nonlinearities increases, these approaches quickly grow in complexity. Further, the inclusion of memory effects in a Volterra-series approach would cause an exponential increase in complexity.